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In this week’s episode of the Radar Podcast, O’Reilly’s Mary Treseler chats with Mike Kuniavsky, a principal scientist in the Innovation Services Group at PARC. Kuniavsky talks about designing for the Internet of Things ecosystem and why the most interesting thing about the IoT isn’t the “things” but the sensors. He also talks about his deep-seated love for appliances and furniture, and how intelligence will affect those industries.
Here are some highlights from their conversation:
Wearables as a class is really weird. It describes where the thing is, not what it is. It’s like referring to kitchenables. ‘Oh, I’m making a kitchenable.’ What does that mean? What does it do for you?
There’s this slippery slope between service design and UX design. I think UX design is more digital and service design allows itself to include things like a poster that’s on a wall in a lobby, or a little card that gets mailed to people, or a human being that they can talk to. … Service design takes a slightly broader view, whereas UX design is — and I think usefully — still focused largely on the digital aspect of it.
I have a deep, long-seated love for appliances and for furniture because they are the tools of our everyday lives, and if anything becomes the content of this new Internet of Things thing first, it’s them. What’s interesting to me is that they have an already existing set of affordances, which means people know what to do with them and how to do it. They have a set of expectations, and how this set of things can now utilize this amazing set of sensing and actuation and meaning-making and statistical analysis technologies that are available up in the cloud, to do the things that they have always done, but do it better. I’m really interested in how intelligence affects the appliance industry.
There are ways to spin the IoT as an Orwellian cyberpunk anti-future, things that spy on you from every corner. They will do that, but I’m not that interested in that aspect of it. I think, actually, that humans are pretty good at negotiating their technologies, even though it sometimes takes a while.
The thing that is happening right now is that by connecting all of these different sensing devices, you turn that sensor input from this very simple gas gauge-like thing that might be useful to somebody in one situation, to a sequence of knowledge that can be modeled and can be much more broadly useful, especially when you have many, many different sources of information that are coming together. That, to me, is a tectonic shift because now you can essentially reason on a giant quantity of information, but the end points that are collecting this information or acting on it can be incredibly small and thin. You get the full power of these enormous artificial intelligence systems, machine learning systems, but without any of the computational overhead or cost, locally. That is really powerful. Every single little thing becomes as powerful as the most powerful computer on earth, and can then anticipate, compensate, and work together with other things in ways that were inconceivable before this shift.
What I’m interested in, broadly speaking, is predictive analytics — I should say, machine learning, statistical modeling, but specifically in predictive statistical modeling, predictive machine learning. I think, really, that is the new super power.
Image by Daniel Mayer on Wikimedia Commons.